Quantitative Analysis Using Structural Equations

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Quantitative Analysis Using Structural Equation Modeling

Prelude - What is Structural Equation Modeling (SEM)? pdf video

About this Site and How to Use

USGS scientists have been involved for a number of years in the development and use of Structural Equation Modeling (SEM). This methodology represents an approach to statistical modeling that focuses on the study of complex cause-effect hypotheses about the mechanisms operating in systems.More...

What's New?

Currently, these web pages represent the intial public launch of education materials. Updates that follow will be logged here.More...

Feature: Causal networks clarify productivity-richness interrelations, bivariate plots do not

Bogong subalpine grassland in Australia
Photo: Bogong subalpine grassland in Australia, a participating site in the Nutrient Network Global Cooperative. Photo credit: Eric Land, Nutrient Network.

Lay summary from Functional Ecology:

Species diversity and productivity are among the most fundamental characteristics of ecosystems. While the importance of these ecological properties is universally agreed upon, the mechanisms connecting these two variables have been debated for decades without resolution. In an attempt to achieve a synthetic understanding of the collective effects of proposed mechanisms, some ecologists have turned to the examination of bivariate plots to see if particular patterns are consistently observed in nature.

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Projects

A number of projects involving USGS scientists at the National Wetlands Research Center apply SEM to basic and applied science issues.More...

Publications

  • Grace, J.B., Scheiner, S.M., Schoolmaster, D.R. Jr. 2015. Structural equation modeling: building and evaluating causal models. Chapter 8 In: Fox, G.A., Negrete-Yanlelevich, S., and Sosa, V.J. (eds.) Ecological Statistics: Contemporary Theory and Application. Oxford University Press.
  • Grace, J.B., Adler, P.B., Harpole, W.S., Borer, E.T., and Seabloom, E.W. 2014 Causal networks clarify productivity–richness interrelations, bivariate plots do not. Functional Ecology, DOI: 10.1111/1365-2435 (early online) (http://onlinelibrary.wiley.com/doi/10.1111/1365-2435.12269/abstract)
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Applications

View a list of applications using SEM. More...
I. Introduction and Background
SEM Essentials
Summary Points (SEM.1.1) pdf
Anatomy of SE Models (SEM.1.2) pdf
Model Specifications (SEM.1.3) pdf
Estimation (SEM.1.4) pdf
Path Rules (SEM.1.5) pdf
Interpreting Coefficients (SEM.1.6) pdf
Categorical Predictors (SEM.1.7) pdf code data
Doing SEM in R
Introduction to Lavaan (SEM.2.1) pdf code data
Local Estimation of Equations (SEM.2.2) pdf code data
Model Evaluation (SEM.3) pdf code data
II. Basic Elements of Modeling
Overview of the Modeling Process (SEM.4) pdf
The Test of Mediation (SEM.5) pdf code data
Test of Mediation Exercise pdf code data
SEM versus Multiple Regression (SEM.6) pdf code data
Causal Modeling Principles Revisited (SEM.7) pdf
SEM versus ANOVA and ANCOVA (SEM.8) pdf
III. Modeling with Latent and Composite Variables
Modeling with Latent Variables (SEM.9) pdf code data
Composites and Formative Indicators (SEM.10.1) pdf code
Composites and Endogenous Nonlinearities (SEM.10.2) pdf
Composites with Multiple Effects (SEM.10.3) pdf
Composites - Comparing Specifications (SEM.10.4) pdf
IV. Additional Topics
Additional lavaan Options pdf
Modeling Interactions pdf code data
Spatial Autocorrelation Procedures pdf code data
Spatial Autocorrelation Exercise pdf code data
Reciprocal Effects Overview pdf
Adjusting for Nested Data using lavaan.survey pdf code data

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